Feature and channel selection for designing a regression-based continuous-variable emotion recognition system with two EEG channels

Javidan, Mahshad; Yazdchi, Mohammadreza; Baharlouei, Zahra; Mahnam, Amin

doi:10.1016/j.bspc.2021.102979

Cited by 22 publications

(11 citation statements)

References 34 publications

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“…Which are from emotion component regions such as the middle left and right hemispheres, as well as frontal and parietal lobes (Wang et al, 2019 ). Other authors have also used these channels, such as F7, F8, and T7–FC2 (Javidan et al, 2021 ); FP1, T7 and T8 on γ, FC6 on β (Guo et al, 2022 ); O2, T8, FC5, and P7 (Dura and Wosiak, 2021 ); FP1–F7 (Taran and Bajaj, 2019 ); F7, FC5, FC6, O2, and P7 (Wang et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Blanco-Ríos,

Candela-Leal,

Orozco-Romo

et al. 2024

Front. Hum. Neurosci.

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Within the field of Humanities, there is a recognized need for educational innovation, as there are currently no reported tools available that enable individuals to interact with their environment to create an enhanced learning experience in the humanities (e.g., immersive spaces). This project proposes a solution to address this gap by integrating technology and promoting the development of teaching methodologies in the humanities, specifically by incorporating emotional monitoring during the learning process of humanistic context inside an immersive space. In order to achieve this goal, a real-time emotion recognition EEG-based system was developed to interpret and classify specific emotions. These emotions aligned with the early proposal by Descartes (Passions), including admiration, love, hate, desire, joy, and sadness. This system aims to integrate emotional data into the Neurohumanities Lab interactive platform, creating a comprehensive and immersive learning environment. This work developed a ML, real-time emotion recognition model that provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds. Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective best band powers were extracted; furthermore, multiple models were evaluated using shift-based data division and cross-validations. After assessing their performance, Extra-Trees achieved a general accuracy of 94%, higher than the reported in the literature (88% accuracy). The proposed model provided real-time predictions of VAD variables and was adapted to classify Descartes' six main passions. However, with the VAD values obtained, more than 15 emotions can be classified (reported in the VAD emotion mapping) and extend the range of this application.

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Section: Resultsmentioning

confidence: 99%

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Blanco-Ríos,

Candela-Leal,

Orozco-Romo

et al. 2024

Front. Hum. Neurosci.

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“…Now, we have trained the LR-based model by estimating these unknown parameters using a maximum likelihood estimator over the training dataset. Using these estimated parameters, we have predicted the class label Classifiers Search range of each parameter GPC kernel= ("RBF", "DotProducts", "RationalQuadratic"), length-scale= (1 to 5), alpha= (0.04, 0.05, 0.06), sigma= (0.01, 0.02, 0.03, 0.05, 0.06, 0.07, 0.08, 0.09) RF max_depth= (2,3,5, None), n_estimators= (15,30,60,120), min_samples_split= (2,3,10), min_samples_leaf= (1,3,10), bootstrap= ("True", "False"), criterion= ("gini", "entropy") k-NN n_neighbors= (2 to 13), leaf_size= (4,5,6) MLP hidden_layer_sizes= [(120,120,50), (60,120,50), (60, 240, 100)), activation= ('relu','tanh','logistic'), alpha= (0.01, 0.05, 0.001), solver= ('adam'), learning_rate= ("constant", "adaptive"] DT max_features= ("auto", "sqrt", "log2"), min_samples_split= (2 to 15), min_samples_leaf= (1 to 11) LR None or response variable (here, ADHD and healthy controls) over the test dataset and also computed the probability of the response variable or class label.…”

Section: ) Logistic Regressionmentioning

confidence: 99%

“…Because, it can provide mainly three types of benefits such as (i) reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant, (ii) helps to improve the performance, and (iii) reduce the setup time in some applications [26]. Moreover, the channel selection approach was used as an effective tool by many researchers in different fields, such as EEG emotion [26]- [30], personal identification [31], [32], user identification [33], seizure detection [34], [41], intruder detection [35], screening of alcoholism [36], depression detection [39], [40], detecting drowsiness [42], auditory attention detection [37], [38], brain-computer interfaces [43], [44] and so on. It was noted that several studies proposed effective predictive-based approaches for the detection of children with ADHD without selecting potential channels from EEG signals [4], [12]- [14], [24], [25], [45]- [48].…”

Section: Introductionmentioning

confidence: 99%

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

et al. 2023

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A TTENTION deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems.To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously published values in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.

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“…These Strategies are tested on a subset of the DEAP dataset. Similarly, [151] performs valence recognition experiments both on DEAP and self-collected data, selecting the channels having the highest features ranks. In contrast, in [152], the channels are scored relying on the average feature weights.…”

Section: Relief-basedmentioning

confidence: 99%

“…In contrast, in [152], the channels are scored relying on the average feature weights. ReliefF is also tested in [153] and [154] on proprietary datasets, and in [151], [155].…”

Section: Relief-basedmentioning

confidence: 99%

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

et al. 2022

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The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.INDEX TERMS Emotion, EEG, channel selection, machine learning, neurophysiology of emotions, wearable devices.

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Feature and channel selection for designing a regression-based continuous-variable emotion recognition system with two EEG channels

Cited by 22 publications

References 34 publications

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms

Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

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